TOPS-SCHOOL WSIM (GLDAS)

Lessons Detailing Exploratory Drought and Flood Analysis

Joshua Brinks

Research Scientist, ISciences, LLC

2024-01-09

Presentation Outline

  • Module Goals
  • Learning objectives
  • Open Science Components
  • Technical details
  • Proposed analyses and outputs

Lesson Goals

  • The objective of this lesson is to provide content for learning water resource issues through data exploration and analysis.
  • We are introducing 3 datasets for this module
    • MODIS NRT Flood Data
    • NYC Lead Data
    • WSIM-GLDAS

Open Science Components

Like all other components of the SCHOOL project, the water resource modules are entirely open source.

  • Open source computing (R, Rstudio, VS Code, QGIS, etc.)
  • Open source datasets; freely available and well documented
  • Open source development
  • Open source “results” on GitHub Pages

Narrative Objectives

We are currently in the early development phases and need to adjust the narrative voice between 3 learning/content components.

  • Water resources
  • Technical data
  • Technical coding

Water Resource Narrative

These include introducing the domain knowledge and human narrative that can be derived from these datasets.

  • Understanding the socioeconomic and environmental impacts of droughts and floods.
  • Exploring drought and flooding events in areas of interest to you and learning about the impacts to local water supplies, agriculture, recreation, and tourism.
  • Basic background on the water cycle.

Technical Data Narrative

  • What does water resource data “look” like?
  • Where do you find it and how do you get it?

Coding Narrative

Are we interested at all in teaching people how to code? Unlikely but then how do you address all the code.

# generate a vector of dates for subsetting
keeps<-seq(lubridate::ymd("2000-01-01"),
           lubridate::ymd("2014-12-01"), 
           by = "month")
           
# filter using that vector
wsim_gldas_anoms <- dplyr::filter(wsim_gldas_anoms, time %in% keeps)

# verify the time dimension was properly subsetted
print(wsim_gldas_anoms)

# do a visual check with the first 6 time-steps
wsim_gldas_anoms |>
  dplyr::slice(index = 1:6, along = "time") |>
  plot(key.pos = 1)

WSIM-GLDAS

WSIM-GLDAS is an open source dataset that characterizes surpluses and deficits of freshwater and the parameters determining these anomalies.

  • Globally rasterized dataset issued monthly for 1948-2014
  • Composite surplus/deficit anomalies
  • Multitude of additional metrics and integration periods
  • Freely available on SEDAC

Outputs and Analyses

Whatever the chosen narrative voice and content, we hope to bring greater understanding for each module through visualizations and analysis. The WSIM-GLDAS water resource modules will achieve this by creating:

  • National and regional 12 month integration composite surplus/deficit maps
  • Time series illustrations of point locations
  • Population exposure time series figures and tables

Composite Surplus and Deficit Maps

Twelve month integration maps illustrate the observed drought or flooding of an area relative to a long term baseline period.

Composite Surplus and Deficit Maps (cont.)

Now you can zoom in on an area of interest with a monthly time series.

Location of Interest

Point location time series figures illustrate long term trends for a single location on a month to month basis.

Population Exposure

Population exposure plots and tables help illustrate the sociological impacts of droughts and floods.

Current Drafts

Although the module and lesson structure is yet to be finalized, we have some rough drafts in place that demonstrate the baseline technical workflow and present a template for dev assistants.

An Open Process

This is an open process reliant on feedback from our:

  • Subject matter experts
  • Project managers
  • Development team
  • Research assistants
  • Users and collaborators like you

We always welcome comments, contributors, and all types of feedback in person, through the TOPS network, and our GitHub repositories.

Acknowledgements & Questions

Thanks to everyone at NASA-TOPS, Columbia, ISciences, and the TOPS-SCHOOL team.